## [1] 3 4 5
## [1] 4
## [1] 4
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
# Find out more about functions or data
?mtcars
?mean
# Look at a single column with $
print(mtcars$mpg)## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2
## [15] 10.4 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4
## [29] 15.8 19.7 15.0 21.4
## [1] 20.09062
tidyverse packageTip: when you open quotation marks "", hit TAB to navigate to a file.
Looking at our data
## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
ggplotAdding more and assigning to an object, p:
# Adding more and assigning to p
p <- ggplot(data = gapminder) +
aes(x = lifeExp,
y = gdpPercap,
colour = continent,
size = pop) +
geom_point(alpha = 0.3) +
geom_smooth() +
scale_y_log10() +
facet_wrap(~continent)
# Plotting p
pplotlyInstall:
Load:
Take our p plot and put it inside ggplotly:
Install required packages.
Load gganimate package. The gifski and png packages are background packages used by gganimate and don’t need to be loaded:
Create a normal ggplot and add the transition_time line to animate:
dancing_gapminder <- ggplot(data = gapminder) +
aes(x = lifeExp,
y = gdpPercap,
colour = continent) +
geom_point(size = 3) +
scale_y_log10() +
facet_wrap(~continent) +
labs(title = "Year {round(frame_time, 0)}") +
# This is the animation part:
transition_time(year)
# View your animation
dancing_gapminder
# Save your animation
anim_save("atlas/dancing_gapminder.gif")Adding a column
Filter to keep only 2007
Drop the variable gdpPercap
sf objectWe use the sf package to wrangle mapping data:
Load it:
Read gapminder + geometry data:
Look at the names of the sf object:
## [1] "country" "continent" "year" "lifeExp" "pop" "gdpPercap"
## [7] "geometry"
We can use pipes %>% and filter to look at specific areas (or we can remove areas). For example, we can remove Antarctica:
gapmap %>%
filter(country != "Antarctica") %>%
ggplot() +
aes(geometry = geometry,
fill = gdpPercap) +
geom_sf(lwd = 0)Or only include the continent ‘Americas’:
gapmap %>%
filter(continent == "Americas") %>%
ggplot() +
aes(geometry = geometry,
fill = gdpPercap) +
geom_sf(lwd = 0)gridExtraWe can generate multiple plots and present them together using the gridExtra package:
And load it:
Create the first plot and assign to plot1
# Plot 1: americas by gdpPercap
plot1 <- gapmap %>%
filter(continent == "Americas") %>%
ggplot() +
aes(geometry = geometry,
fill = gdpPercap/1000) + # GDP per capita in thousands
geom_sf(lwd = 0) +
coord_sf(datum = NA) +
theme_void() +
theme(legend.position = "bottom") +
labs(subtitle = "GDP per capita (thousands)",
fill = "")And the second plot assigned to plot2:
# Plot 2: americas by gdpPercap
plot2 <- gapmap %>%
filter(continent == "Americas") %>%
ggplot() +
aes(geometry = geometry,
fill = lifeExp) +
geom_sf(lwd = 0) +
coord_sf(datum = NA) +
theme_void() +
theme(legend.position = "bottom") +
labs(subtitle = "Life expectancy",
fill = "")Then use grid.arrange to arrange the plots side-by-side with a title at the top:
# Arrange the two plots side-by-side:
americas_map <- grid.arrange(plot1, plot2, ncol = 2,
top = "GDP and life expectancy in the Americas, 2007")